HELM Evaluation Platform

• HELM Evaluation Platform is a unified framework that organizes model assessment along scenarios and metrics to ensure comprehensive and comparable evaluations.
• It employs a top-down taxonomy and multi-metric evaluation covering accuracy, robustness, fairness, bias, and efficiency across diverse AI domains.
• The platform promotes transparency with open data releases and reproducible benchmarks, driving community standards in real-world AI research.

The HELM Evaluation Platform refers to the framework, methodologies, toolkits, and benchmark suites designed for the comprehensive, systematic, and transparent assessment of advanced systems, with notable focus areas including LLMs, structured software artefacts (such as Helm charts for Kubernetes), vision-LLMs, real-world medical AI, and related domains. Variants and extensions (e.g., SEA-HELM, VHELM, MedHELM) address specific subfields or modalities, but all adhere to the foundational HELM principles of holistic evaluation, modular taxonomy, and public transparency.

1. Foundational Principles and Taxonomy

HELM (Holistic Evaluation of LLMs) was conceived to address the fragmentation and lack of comparability in LLM evaluation, where ad-hoc or scenario-limited benchmarks dominated. Its defining contributions are:

• Top-down taxonomy: Instead of adding benchmarks and metrics incrementally, HELM first taxonomizes the evaluation space along two axes:
  • Scenarios: Broad application-driven tasks and datasets (e.g., reading comprehension, reasoning, toxicity detection, question answering).
  • Metrics: Multiple desiderata or qualities (accuracy, calibration, robustness, fairness, bias, toxicity, efficiency).
• Explicit scenario-metric mapping: Every scenario is evaluated by a structured set of metrics, exposing not only what is measured but also critical gaps (e.g., neglected dialects, unmeasured trustworthiness) (2211.09110).

This explicit taxonomy underlies all subsequent specialized HELM suites (see Table 1).

Sub-Platform	Core Axis	Modalities / Focus
HELM	LM scenarios/metrics	LLMs (text, code, etc.)
VHELM	9 vision axes	Vision-LLMs (image+text)
MedHELM	5 medical categories	Medical LLM application
SEA-HELM	5 cultural pillars	Southeast Asian language/culture
HELMET	7 context categories	Long-context LLMs

Each applies the taxonomy approach contextualized for its domain.

2. Multi-Metric and Modular Design

HELM prescribes evaluation along multiple metrics per scenario, not only accuracy. For example:

1. Accuracy: Correctness of response.
2. Calibration: Alignment of model confidence with actual correctness.
3. Robustness: Stability under adversarial or distributional shifts.
4. Fairness: Equitability across user groups or conditions.
5. Bias: Propensity for undesirable social outcomes.
6. Toxicity: Tendency to generate harmful or inappropriate content.
7. Efficiency: Resource or time/cost requirements.

Thus, each evaluated model generates a multi-dimensional measurement profile, enabling the systematic exposure of trade-offs (e.g., a model may score highly for accuracy, but poorly for fairness or robustness).

Toolkit implementation:

• Highly extensible and modular, supporting addition of new models, datasets, metrics, and adaptation methods.
• Full release of all model prompts/completions for reproducibility and audit.

3. Evaluation Workflow and Empirical Methodology

The HELM platform conducts standardized, scalable evaluations by unifying scenario and metric coverage across models and modalities. The canonical workflow is:

1. Scenario selection: Curate a diverse, representative set of real-world use cases (e.g., question answering, summarization, multi-turn dialogue, etc.).
2. Model selection: Include as many prominent models as feasible (open- and closed-source, various sizes/architectures).
3. Metric evaluation: Apply the full metric suite to each (model, scenario) pair under controlled conditions.
4. Systematic reporting: Aggregate results, generate summary tables, and highlight key findings (e.g., top-level insights, per-metric trade-offs).

For specialized domains:

• VHELM applies this pipeline to vision-LLMs, evaluating not only technical skills but also multilinguality, fairness, bias, toxicity, and safety.
• MedHELM builds a clinician-validated taxonomy (5 categories, 22 subcategories, 121 tasks), ensures coverage of real-world medical workflows beyond knowledge recall, and uses an LLM-jury rating system validated against human clinicians for open-ended tasks (2505.23802).
• SEA-HELM targets regional language/cultural benchmarks, aggregating results with normalized, language-aware metrics (2502.14301).
• HELMET focuses on long-context LLMs, using model-based evaluation, robust prompting, and application-diverse categories to distinguish model capabilities at high token lengths (2410.02694).

4. Transparency, Public Resources, and Community Standards

HELM and its extensions emphasize transparency and reproducibility via:

• Open releases of raw evaluation data: Prompts, model outputs, and results for each (model, scenario) combination.
• Open-source codebases: Enabling extension (new scenarios, models, and metrics), re-evaluation, or additional analysis (e.g., stanford-crfm/helm, crfm.stanford.edu/helm).
• Public leaderboards: For monitoring progress, reproducible cross-model and cross-benchmark comparisons, and community submissions (e.g., SEA-HELM leaderboard).
• Standardized adaptation and prompt engineering: To avoid methodological bias and ensure model outputs are comparable.

By releasing both the code and evaluation artefacts, HELM serves as an evolving, living benchmark—open to community-driven updates in tasks, languages, and models.

5. Empirical Findings and Impact Across Domains

Aggregated findings from HELM-driven evaluations have surfaced key insights:

• No universally dominant model: Model strengths are scenario- and metric-specific; high-performing models in accuracy may underperform in fairness, robustness, or safety.
• Substantial open–closed model gaps: In various domains (LLMs, VLMs, medical AI), closed-source frontier models often substantially outperform current open-weight ones, especially for complex reasoning, context, or safety-sensitive tasks.
• Importance of comprehensive benchmarking: Synthetic or context-limited tasks are often poor predictors of real-world model deployment quality. Application-driven categories, relevant metrics, and robust prompting are all critical for accurate model ranking and capability discovery.
• Task- or region-specific challenges: For example, even state-of-the-art models display major weaknesses in SEA language/culture tasks, long-context full-document reasoning, or administrative medical tasks, which are not detected by generic or exam-centric evaluation.

6. Evolving Methodologies and Extensions

The platform is explicitly designed as a living benchmark:

• New scenarios and tasks (e.g., RAG, full-context QA, region/culture-specific evaluation, safety, administrative complexity) are regularly added to track advances in modeling and emergent challenges.
• Metric suites are updated to reflect community concerns (e.g., climate impact, safety, user alignment, multimodal QA).
• Specialized modules (e.g., LLM-jury systems for free-text assessment, expert mixture scoring, language-region normalization) allow for adaptation to new technical or sociotechnical requirements.

A plausible implication is that HELM’s extensibility and public design features ensure its continued relevance as both models and sociotechnical needs evolve.

7. Technical Implementation and Metrics

All HELM and subdomain platform score calculations are explicit and reproducible:

• General formula for normalized score in SEA-HELM:

$$\text{normalized\_score} = \frac{x - \text{random\_baseline}}{\text{max} - \text{random\_baseline}} \times 100$$

• Medical open-ended metric (LLM-jury, MedHELM):

$$\mathrm{ICC}(3, k) = \frac{MS_{\text{cases}} - MS_\varepsilon}{MS_{\text{cases}}}$$

• VHELM win rate:

$$\text{Win Rate}_m = \frac{1}{N-1} \sum_{m' \neq m} \mathbb{1}_{\text{score}_m > \text{score}_{m'}}$$

• Efficiency metric (HELM): Inference time, compute, or other resource cost per scenario/metric pair, always reported alongside behavioral metrics.

Benchmarks strictly document and aggregate at the task, scenario, model, and metric level, ensuring that all reported outcomes are methodologically sound and comparable.

---

In summary, the HELM Evaluation Platform—a family of modular, extensible, and community-driven evaluation frameworks—establishes rigorous, transparent standards for the holistic assessment of models and artefacts in language, vision, medicine, sociotechnical systems, and beyond. Its design ensures task diversity, metric comprehensiveness, standardized adaptation, and public accessibility, positioning HELM as a reference benchmark for both foundational research and practical, real-world AI deployment.